On February 10, the Defense Advanced Research Projects Agency (DARPA) issued an opportunity for the Compositional Learning-And-Reasoning for AI Complex Systems Engineering (CLARA) program. Responses are due by 4:00 p.m. Eastern on April 10, according to SAM.gov.
DARPA’s Defense Sciences Office (DSO) is issuing a Disruption Opportunity (DO), inviting submissions of innovative basic or applied research concepts in the technical domain of high assurance artificial intelligence systems.
This DO is issued under the Program Announcement for Disruptioneering, DARPA-PA-25-07. All awards will be made in the form of an Other Transaction (OT) for Prototype project. The total award value for the combined Phase 1 base (Feasibility Study) and Phase 2 option (Proof of Concept) is limited to $2,000,000. This total award value includes Government funding and performer cost share if required or proposed.
CLARA is an exploratory, fundamental research program that aims to create high assurance, broadly applicable AI systems of systems. This will be accomplished by creating a scientific, theory-driven architectural foundation for the hierarchical composition of a wide variety of both Machine Learning (ML) and Automated Reasoning (AR), also known as Knowledge Representation and Reasoning, subsystems. CLARA seeks to create a foundation that is algorithmic, highly reusable, and computationally scalable.
For CLARA, AI systems of systems means systems that are hierarchically composed of constituent subsystems that employ ML and AR techniques and also contain both ML and AR models whose outputs are composed to create the overall system response. Assurance here means verifiability with strong explainability to humans, based on automated logical proofs and hierarchical, vetted logic building blocks.
DARPA anticipates that successful performers in this program will combine innovations such as higher order logic, probabilistic logic, logical expressivity, hierarchically structured knowledge representation, and interoperable integration of AR and ML. The range of practically important ML and AR techniques and associated model families that will be explored within this program as constituents for hierarchical composition includes, but is not limited to, Neural Networks (NN), Bayesian ML, Reinforcement Learning (RL), Generalized Additive Models (GAM), Logic Programs (LP), Classical logic, and Answer Set Programs (ASP).
Review the DARPA CLARA opportunity.
Source: SAM
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